app.py

import gradio as gr

from transformers import AutoConfig,ViTImageProcessor,ViTForImageClassification,AutoModel
import base64
import os

processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224')
model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')
images = 'room.jpg'


def image_classifier(image):
    inputs = processor(images=image, return_tensors="pt")
    outputs = model(**inputs)
    logits = outputs.logits

    logits_np = logits.detach().cpu().numpy()
    logits_args = logits_np.argsort()[0][-3:]

    prediction_classes = [model.config.id2label[predicted_class_idx] for predicted_class_idx in logits_args ]
    
    result = {}
    for i,item in enumerate(prediction_classes):
        result[item] = logits_np[0][i]

    return result


with gr.Blocks(title="Image Classification using Google Vision Transformer") as demo :
    gr.Markdown(
    """ 
    <center>
    <h1>
    The Vision Transformer (ViT) 
    </h1>
    Transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. 
    Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.    
    </center>
    """
    )
    with gr.Row():
        with gr.Column():
            # inputt = gr.inputs.Image(shape=(200, 200)),
            inputt = gr.Image(type="numpy", label="Input Image for Classification")
            button = gr.Button(value="Classify")
        with gr.Column():
            output = gr.Label()
        button.click(image_classifier,inputt,output)

demo.launch()